# tf.random.fixed_unigram_candidate_sampler

Samples a set of classes using the provided (fixed) base distribution.

This operation randomly samples a tensor of sampled classes (`sampled_candidates`) from the range of integers `[0, range_max)`.

See the Candidate Sampling Algorithms Reference for a quick course on Candidate Sampling.

The elements of `sampled_candidates` are drawn without replacement (if `unique=True`) or with replacement (if `unique=False`) from the base distribution.

The base distribution is read from a file or passed in as an in-memory array. There is also an option to skew the distribution by applying a distortion power to the weights.

In addition, this operation returns tensors `true_expected_count` and `sampled_expected_count` representing the number of times each of the target classes (`true_classes`) and the sampled classes (`sampled_candidates`) is expected to occur in an average tensor of sampled classes. These values correspond to `Q(y|x)` defined in the Candidate Sampling Algorithms Reference. If `unique=True`, then these are post-rejection probabilities and we compute them approximately.

Note that this function (and also other `*_candidate_sampler` functions) only gives you the ingredients to implement the various Candidate Sampling algorithms listed in the big table in the Candidate Sampling Algorithms Reference. You still need to implement the algorithms yourself.

For example, according to that table, the phrase "negative samples" may mean different things in different algorithms. For instance, in NCE, "negative samples" means `S_i` (which is just the sampled classes) which may overlap with true classes, while in Sampled Logistic, "negative samples" means `S_i - T_i` which excludes the true classes. The return value `sampled_candidates` corresponds to `S_i`, not to any specific definition of "negative samples" in any specific algorithm. It's your responsibility to pick an algorithm and calculate the "negative samples" defined by that algorithm (e.g. `S_i - T_i`).

As another example, the `true_classes` argument is for calculating the `true_expected_count` output (as a by-product of this function's main calculation), which may be needed by some algorithms (according to that table). It's not for excluding true classes in the return value `sampled_candidates`. Again that step is algorithm-specific and should be carried out by you.

`true_classes` A `Tensor` of type `int64` and shape ```[batch_size, num_true]```. The target classes.
`num_true` An `int`. The number of target classes per training example.
`num_sampled` An `int`. The number of classes to randomly sample.
`unique` A `bool`. Determines whether all sampled classes in a batch are unique.
`range_max` An `int`. The number of possible classes.
`vocab_file` Each valid line in this file (which should have a CSV-like format) corresponds to a valid word ID. IDs are in sequential order, starting from num_reserved_ids. The last entry in each line is expected to be a value corresponding to the count or relative probability. Exactly one of `vocab_file` and `unigrams` needs to be passed to this operation.
`distortion` The distortion is used to skew the unigram probability distribution. Each weight is first raised to the distortion's power before adding to the internal unigram distribution. As a result, `distortion = 1.0` gives regular unigram sampling (as defined by the vocab file), and `distortion = 0.0` gives a uniform distribution.
`num_reserved_ids` Optionally some reserved IDs can be added in the range `[0, num_reserved_ids)` by the users. One use case is that a special unknown word token is used as ID 0. These IDs will have a sampling probability of 0.
`num_shards` A sampler can be used to sample from a subset of the original range in order to speed up the whole computation through parallelism. This parameter (together with `shard`) indicates the number of partitions that are being used in the overall computation.
`shard` A sampler can be used to sample from a subset of the original range in order to speed up the whole computation through parallelism. This parameter (together with `num_shards`) indicates the particular partition number of the operation, when partitioning is being used.
`unigrams` A list of unigram counts or probabilities, one per ID in sequential order. Exactly one of `vocab_file` and `unigrams` should be passed to this operation.
`seed` An `int`. An operation-specific seed. Default is 0.
`name` A name for the operation (optional).

`sampled_candidates` A tensor of type `int64` and shape `[num_sampled]`. The sampled classes. As noted above, `sampled_candidates` may overlap with true classes.
`true_expected_count` A tensor of type `float`. Same shape as `true_classes`. The expected counts under the sampling distribution of each of `true_classes`.
`sampled_expected_count` A tensor of type `float`. Same shape as `sampled_candidates`. The expected counts under the sampling distribution of each of `sampled_candidates`.

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{"lastModified": "Last updated 2024-01-23 UTC."}